1
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Chen SCY, Chen Y, Geisler WS, Seidemann E. Neural correlates of perceptual similarity masking in primate V1. eLife 2024; 12:RP89570. [PMID: 38592269 PMCID: PMC11003749 DOI: 10.7554/elife.89570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024] Open
Abstract
Visual detection is a fundamental natural task. Detection becomes more challenging as the similarity between the target and the background in which it is embedded increases, a phenomenon termed 'similarity masking'. To test the hypothesis that V1 contributes to similarity masking, we used voltage sensitive dye imaging (VSDI) to measure V1 population responses while macaque monkeys performed a detection task under varying levels of target-background similarity. Paradoxically, we find that during an initial transient phase, V1 responses to the target are enhanced, rather than suppressed, by target-background similarity. This effect reverses in the second phase of the response, so that in this phase V1 signals are positively correlated with the behavioral effect of similarity. Finally, we show that a simple model with delayed divisive normalization can qualitatively account for our findings. Overall, our results support the hypothesis that a nonlinear gain control mechanism in V1 contributes to perceptual similarity masking.
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Affiliation(s)
- Spencer Chin-Yu Chen
- Center for Perceptual Systems, University of Texas at AustinAustinUnited States
- Department of Psychology, University of Texas at AustinAustinUnited States
- Center for Theoretical and Computational NeuroscienceAustinUnited States
- Department of Neuroscience, University of Texas at AustinAustinUnited States
- Department of Neurosurgery, Rutgers UniversityNew BrunswickUnited States
| | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas at AustinAustinUnited States
- Department of Psychology, University of Texas at AustinAustinUnited States
- Center for Theoretical and Computational NeuroscienceAustinUnited States
- Department of Neuroscience, University of Texas at AustinAustinUnited States
| | - Wilson S Geisler
- Center for Perceptual Systems, University of Texas at AustinAustinUnited States
- Department of Psychology, University of Texas at AustinAustinUnited States
- Center for Theoretical and Computational NeuroscienceAustinUnited States
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas at AustinAustinUnited States
- Department of Psychology, University of Texas at AustinAustinUnited States
- Center for Theoretical and Computational NeuroscienceAustinUnited States
- Department of Neuroscience, University of Texas at AustinAustinUnited States
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2
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Wu J, Chen Y, Veeraraghavan A, Seidemann E, Robinson JT. Mesoscopic calcium imaging in a head-unrestrained male non-human primate using a lensless microscope. Nat Commun 2024; 15:1271. [PMID: 38341403 PMCID: PMC10858944 DOI: 10.1038/s41467-024-45417-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
Mesoscopic calcium imaging enables studies of cell-type specific neural activity over large areas. A growing body of literature suggests that neural activity can be different when animals are free to move compared to when they are restrained. Unfortunately, existing systems for imaging calcium dynamics over large areas in non-human primates (NHPs) are table-top devices that require restraint of the animal's head. Here, we demonstrate an imaging device capable of imaging mesoscale calcium activity in a head-unrestrained male non-human primate. We successfully miniaturize our system by replacing lenses with an optical mask and computational algorithms. The resulting lensless microscope can fit comfortably on an NHP, allowing its head to move freely while imaging. We are able to measure orientation columns maps over a 20 mm2 field-of-view in a head-unrestrained macaque. Our work establishes mesoscopic imaging using a lensless microscope as a powerful approach for studying neural activity under more naturalistic conditions.
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Affiliation(s)
- Jimin Wu
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Yuzhi Chen
- Department of Neuroscience, University of Texas at Austin, 100 E 24th St., Austin, TX, 78712, USA
- Department of Psychology, University of Texas at Austin, 108 E Dean Keeton St., Austin, TX, 78712, USA
| | - Ashok Veeraraghavan
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Department of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Eyal Seidemann
- Department of Neuroscience, University of Texas at Austin, 100 E 24th St., Austin, TX, 78712, USA.
- Department of Psychology, University of Texas at Austin, 108 E Dean Keeton St., Austin, TX, 78712, USA.
| | - Jacob T Robinson
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
- Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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3
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Becker LA, Li B, Priebe NJ, Seidemann E, Taillefumier T. Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs. ArXiv 2023:arXiv:2304.09280v3. [PMID: 37131877 PMCID: PMC10153295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance ≃ 4 - 9 m V 2 ) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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Affiliation(s)
- Logan A. Becker
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
| | - Baowang Li
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Perceptual Systems, The University of Texas at Austin
- Center for Learning and Memory, The University of Texas at Austin
- Department of Psychology, The University of Texas at Austin
| | - Nicholas J. Priebe
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Learning and Memory, The University of Texas at Austin
| | - Eyal Seidemann
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Center for Perceptual Systems, The University of Texas at Austin
- Department of Psychology, The University of Texas at Austin
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
- Department of Neuroscience, The University of Texas at Austin
- Department of Mathematics, The University of Texas at Austin
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4
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Chen SC, Chen Y, Geisler WS, Seidemann E. NEURAL CORRELATES OF PERCEPTUAL SIMILARITY MASKING IN PRIMATE V1. bioRxiv 2023:2023.07.06.547970. [PMID: 37503133 PMCID: PMC10369882 DOI: 10.1101/2023.07.06.547970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Visual detection is a fundamental natural task. Detection becomes more challenging as the similarity between the target and the background in which it is embedded increases, a phenomenon termed "similarity masking". To test the hypothesis that V1 contributes to similarity masking, we used voltage sensitive dye imaging (VSDI) to measure V1 population responses while macaque monkeys performed a detection task under varying levels of target-background similarity. Paradoxically, we find that during an initial transient phase, V1 responses to the target are enhanced, rather than suppressed, by target-background similarity. This effect reverses in the second phase of the response, so that in this phase V1 signals are positively correlated with the behavioral effect of similarity. Finally, we show that a simple model with delayed divisive normalization can qualitatively account for our findings. Overall, our results support the hypothesis that a nonlinear gain control mechanism in V1 contributes to perceptual similarity masking.
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Affiliation(s)
- Spencer C Chen
- Center for Perceptual Systems, University of Texas at Austin
- Center for Theoretical and Computational Neuroscience, University of Texas at Austin
- Department of Psychology, University of Texas at Austin
- Department of Neuroscience, University of Texas at Austin
- Department of Neurosurgery, Rutgers University
| | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas at Austin
- Center for Theoretical and Computational Neuroscience, University of Texas at Austin
- Department of Psychology, University of Texas at Austin
- Department of Neuroscience, University of Texas at Austin
| | - Wilson S Geisler
- Center for Perceptual Systems, University of Texas at Austin
- Center for Theoretical and Computational Neuroscience, University of Texas at Austin
- Department of Psychology, University of Texas at Austin
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas at Austin
- Center for Theoretical and Computational Neuroscience, University of Texas at Austin
- Department of Psychology, University of Texas at Austin
- Department of Neuroscience, University of Texas at Austin
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5
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Becker LA, Li B, Priebe NJ, Seidemann E, Taillefumier T. Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs. bioRxiv 2023:2023.04.17.536739. [PMID: 37131647 PMCID: PMC10153111 DOI: 10.1101/2023.04.17.536739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance ≅ 4-9mV 2 ) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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6
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Seidemann E. Invited Session IV: Studies of the visual cortex with sub-millimeter resolution: Toward an all-optical bi-directional interrogation of topographic population codes in primate cortex. J Vis 2023; 23:26. [PMID: 37733552 DOI: 10.1167/jov.23.11.26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023] Open
Abstract
The representation of visual stimuli in primate V1 is widely distributed and topographic. This raises the possibility that in some visual tasks, downstream areas that decode V1 signals in order to mediate perception could combine V1 signals at a relevant topographic scale-e.g., at the scale of orientation columns. If this were the case, then the fundamental unit of information would be individual columns rather than single neurons, and to account for the subject's behavior in a perceptual task, it would be necessary and sufficient to consider the summed activity of the thousands of neurons within each column. In this presentation I will discuss our initial attempts to test this topographic-code hypothesis using our optical-genetic toolbox for "reading" and "writing" neural population codes at the spatial scales of topographic maps in V1 of behaving macaques.
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7
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Zhou J, Whitmire M, Chen Y, Seidemann E. Near-additive temporal dynamics of sub-threshold population responses in macaque V1. J Vis 2022. [DOI: 10.1167/jov.22.14.3105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Jingyang Zhou
- Center for Computational Neuroscience, Flatiron Institute
- Center for Neural Science, New York University
| | - Matt Whitmire
- Center for Perceptual Systems, University of Texas, Austin
- Department of Psychology, University of Texas, Austin
- Department of Neuroscience, University of Texas, Austin
| | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas, Austin
- Department of Psychology, University of Texas, Austin
- Department of Neuroscience, University of Texas, Austin
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas, Austin
- Department of Psychology, University of Texas, Austin
- Department of Neuroscience, University of Texas, Austin
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8
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Chen SCY, Benvenuti G, Chen Y, Kumar S, Ramakrishnan C, Deisseroth K, Geisler WS, Seidemann E. Similar neural and perceptual masking effects of low-power optogenetic stimulation in primate V1. eLife 2022; 11:68393. [PMID: 34982033 PMCID: PMC8765749 DOI: 10.7554/elife.68393] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 01/03/2022] [Indexed: 12/29/2022] Open
Abstract
Can direct stimulation of primate V1 substitute for a visual stimulus and mimic its perceptual effect? To address this question, we developed an optical-genetic toolkit to 'read' neural population responses using widefield calcium imaging, while simultaneously using optogenetics to 'write' neural responses into V1 of behaving macaques. We focused on the phenomenon of visual masking, where detection of a dim target is significantly reduced by a co-localized medium-brightness mask (Cornsweet and Pinsker, 1965; Whittle and Swanston, 1974). Using our toolkit, we tested whether V1 optogenetic stimulation can recapitulate the perceptual masking effect of a visual mask. We find that, similar to a visual mask, low-power optostimulation can significantly reduce visual detection sensitivity, that a sublinear interaction between visual- and optogenetic-evoked V1 responses could account for this perceptual effect, and that these neural and behavioral effects are spatially selective. Our toolkit and results open the door for further exploration of perceptual substitutions by direct stimulation of sensory cortex.
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Affiliation(s)
- Spencer Chin-Yu Chen
- Department of Neurosurgery, Rutgers University, New Brunswick, United States.,Center for Perceptual Systems, The University of Texas at Austin, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States
| | - Giacomo Benvenuti
- Center for Perceptual Systems, The University of Texas at Austin, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States
| | - Yuzhi Chen
- Center for Perceptual Systems, The University of Texas at Austin, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States
| | - Satwant Kumar
- Center for Perceptual Systems, The University of Texas at Austin, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States
| | | | - Karl Deisseroth
- CNC Program, Stanford University, Stanford, United States.,Department of Bioengineering, Stanford University, Stanford, United States.,Neurosciences Program, Stanford University, Stanford, United States.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States.,Howard Hughes Medical Institute, Stanford University, Stanford, United States
| | - Wilson S Geisler
- Center for Perceptual Systems, The University of Texas at Austin, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Neurosciences Program, University of Texas, Austin, United States
| | - Eyal Seidemann
- Center for Perceptual Systems, The University of Texas at Austin, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States.,Neurosciences Program, University of Texas, Austin, United States
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9
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Trautmann EM, O'Shea DJ, Sun X, Marshel JH, Crow A, Hsueh B, Vesuna S, Cofer L, Bohner G, Allen W, Kauvar I, Quirin S, MacDougall M, Chen Y, Whitmire MP, Ramakrishnan C, Sahani M, Seidemann E, Ryu SI, Deisseroth K, Shenoy KV. Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nat Commun 2021; 12:3689. [PMID: 34140486 PMCID: PMC8211867 DOI: 10.1038/s41467-021-23884-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/19/2021] [Indexed: 02/05/2023] Open
Abstract
Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.
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Affiliation(s)
- Eric M Trautmann
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA.
| | - James H Marshel
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ailey Crow
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Brian Hsueh
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Sam Vesuna
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Lucas Cofer
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Gergő Bohner
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Will Allen
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Isaac Kauvar
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Sean Quirin
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | - Matthew P Whitmire
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | | | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Karl Deisseroth
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Science, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
| | - Krishna V Shenoy
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Stanford University, Stanford, CA, USA.
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10
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Bai Y, Chen S, Chen Y, Geisler WS, Seidemann E. Similar masking effects of natural backgrounds on detection performances in humans, macaques, and macaque-V1 population responses. J Neurophysiol 2021; 125:2125-2134. [PMID: 33909494 DOI: 10.1152/jn.00275.2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Visual systems evolve to process the stimuli that arise in the organism's natural environment, and hence, to fully understand the neural computations in the visual system, it is important to measure behavioral and neural responses to natural visual stimuli. Here, we measured psychometric and neurometric functions in the macaque monkey for detection of a windowed sine-wave target in uniform backgrounds and in natural backgrounds of various contrasts. The neurometric functions were obtained by near-optimal decoding of voltage-sensitive-dye-imaging (VSDI) responses at the retinotopic scale in primary visual cortex (V1). The results were compared with previous human psychophysical measurements made under the same conditions. We found that human and macaque behavioral thresholds followed the generalized Weber's law as function of contrast, and that both the slopes and the intercepts of the threshold as a function of background contrast match each other up to a single scale factor. We also found that the neurometric thresholds followed the generalized Weber's law with slopes and intercepts matching the behavioral slopes and intercepts up to a single scale factor. We conclude that human and macaque ability to detect targets in natural backgrounds are affected in the same way by background contrast, that these effects are consistent with population decoding at the retinotopic scale by down-stream circuits, and that the macaque monkey is an appropriate animal model for gaining an understanding of the neural mechanisms in humans for detecting targets in natural backgrounds. Finally, we discuss limitations of the current study and potential next steps.NEW & NOTEWORTHY We measured macaque detection performance in natural images and compared their performance to the detection sensitivity of neurophysiological responses recorded in the primary visual cortex (V1), and to the performance of human subjects. We found that 1) human and macaque behavioral performances are in quantitative agreement and 2) are consistent with near-optimal decoding of V1 population responses.
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Affiliation(s)
- Yoon Bai
- Center for Perceptual Systems, University of Texas, Austin, Texas.,Department of Psychology, University of Texas, Austin, Texas
| | - Spencer Chen
- Center for Perceptual Systems, University of Texas, Austin, Texas
| | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas, Austin, Texas
| | - Wilson S Geisler
- Center for Perceptual Systems, University of Texas, Austin, Texas.,Department of Psychology, University of Texas, Austin, Texas
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas, Austin, Texas.,Department of Psychology, University of Texas, Austin, Texas.,Department of Neuroscience, University of Texas, Austin, Texas
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11
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Tremblay S, Acker L, Afraz A, Albaugh DL, Amita H, Andrei AR, Angelucci A, Aschner A, Balan PF, Basso MA, Benvenuti G, Bohlen MO, Caiola MJ, Calcedo R, Cavanaugh J, Chen Y, Chen S, Chernov MM, Clark AM, Dai J, Debes SR, Deisseroth K, Desimone R, Dragoi V, Egger SW, Eldridge MAG, El-Nahal HG, Fabbrini F, Federer F, Fetsch CR, Fortuna MG, Friedman RM, Fujii N, Gail A, Galvan A, Ghosh S, Gieselmann MA, Gulli RA, Hikosaka O, Hosseini EA, Hu X, Hüer J, Inoue KI, Janz R, Jazayeri M, Jiang R, Ju N, Kar K, Klein C, Kohn A, Komatsu M, Maeda K, Martinez-Trujillo JC, Matsumoto M, Maunsell JHR, Mendoza-Halliday D, Monosov IE, Muers RS, Nurminen L, Ortiz-Rios M, O'Shea DJ, Palfi S, Petkov CI, Pojoga S, Rajalingham R, Ramakrishnan C, Remington ED, Revsine C, Roe AW, Sabes PN, Saunders RC, Scherberger H, Schmid MC, Schultz W, Seidemann E, Senova YS, Shadlen MN, Sheinberg DL, Siu C, Smith Y, Solomon SS, Sommer MA, Spudich JL, Stauffer WR, Takada M, Tang S, Thiele A, Treue S, Vanduffel W, Vogels R, Whitmire MP, Wichmann T, Wurtz RH, Xu H, Yazdan-Shahmorad A, Shenoy KV, DiCarlo JJ, Platt ML. An Open Resource for Non-human Primate Optogenetics. Neuron 2020; 108:1075-1090.e6. [PMID: 33080229 PMCID: PMC7962465 DOI: 10.1016/j.neuron.2020.09.027] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/28/2020] [Accepted: 09/21/2020] [Indexed: 12/26/2022]
Abstract
Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.
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Affiliation(s)
- Sébastien Tremblay
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Leah Acker
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Arash Afraz
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Daniel L Albaugh
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Hidetoshi Amita
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ariana R Andrei
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Alessandra Angelucci
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Amir Aschner
- Dominik P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Puiu F Balan
- Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000 Leuven, Belgium
| | - Michele A Basso
- Departments of Psychiatry and Biobehavioral Sciences and Neurobiology, UCLA, Los Angeles, CA 90095, USA
| | - Giacomo Benvenuti
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Martin O Bohlen
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Michael J Caiola
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Roberto Calcedo
- Gene Therapy Program, Department of Medicine, University of Pennsylvania, Philadelphia, PA 19014, USA
| | - James Cavanaugh
- Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, MD 20982, USA
| | - Yuzhi Chen
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Spencer Chen
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Mykyta M Chernov
- Division of Neuroscience, Oregon National Primate Resource Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Andrew M Clark
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Ji Dai
- CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen 518055, China
| | - Samantha R Debes
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Karl Deisseroth
- Neuroscience Program, Departments of Bioengineering, Psychiatry, and Behavioral Science, Wu Tsai Neurosciences Institute, Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Robert Desimone
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Seth W Egger
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Mark A G Eldridge
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892, USA
| | - Hala G El-Nahal
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Francesco Fabbrini
- Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Frederick Federer
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Christopher R Fetsch
- The Solomon H. Snyder Department of Neuroscience & Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Michal G Fortuna
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany
| | - Robert M Friedman
- Division of Neuroscience, Oregon National Primate Resource Center, Oregon Health and Science University, Beaverton, OR 97006, USA
| | - Naotaka Fujii
- Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Alexander Gail
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany; Bernstein Center for Computational Neuroscience, Göttingen, Germany; Faculty for Biology and Psychology, University of Göttingen, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Adriana Galvan
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Supriya Ghosh
- Department of Neurobiology and Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL 60637, USA
| | - Marc Alwin Gieselmann
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Roberto A Gulli
- Zuckerman Institute, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - Okihide Hikosaka
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eghbal A Hosseini
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Xing Hu
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Janina Hüer
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany
| | - Ken-Ichi Inoue
- Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan; PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan
| | - Roger Janz
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Mehrdad Jazayeri
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rundong Jiang
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Niansheng Ju
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Kohitij Kar
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Carsten Klein
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Adam Kohn
- Dominik P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Misako Komatsu
- Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Kazutaka Maeda
- Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Julio C Martinez-Trujillo
- Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Masayuki Matsumoto
- Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan; Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
| | - John H R Maunsell
- Department of Neurobiology and Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL 60637, USA
| | - Diego Mendoza-Halliday
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ilya E Monosov
- Department of Neuroscience, Biomedical Engineering, Electrical Engineering, Neurosurgery and Pain Center, Washington University, St. Louis, MO 63110, USA
| | - Ross S Muers
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Lauri Nurminen
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Michael Ortiz-Rios
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany; Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Daniel J O'Shea
- Department of Electrical Engineering, Wu Tsai Neurosciences Institute, and Bio-X Institute, and Neuroscience Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Stéphane Palfi
- Department of Neurosurgery, Assistance Publique-Hopitaux de Paris (APHP), U955 INSERM IMRB eq.15, University of Paris 12 UPEC, Faculté de Médecine, Créteil 94010, France
| | - Christopher I Petkov
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Sorin Pojoga
- Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas-Houston, Houston, TX 77030, USA
| | - Rishi Rajalingham
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Charu Ramakrishnan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Evan D Remington
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cambria Revsine
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA; Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA
| | - Anna W Roe
- Division of Neuroscience, Oregon National Primate Resource Center, Oregon Health and Science University, Beaverton, OR 97006, USA; Interdisciplinary Institute of Neuroscience and Technology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310029, China; Key Laboratory of Biomedical Engineering of the Ministry of Education, Zhejiang University, Hangzhou 310029, China
| | - Philip N Sabes
- Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Richard C Saunders
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892, USA
| | - Hansjörg Scherberger
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany; Bernstein Center for Computational Neuroscience, Göttingen, Germany; Faculty for Biology and Psychology, University of Göttingen, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Michael C Schmid
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK; Department of Neurosciences and Movement Sciences, Faculty of Science and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Wolfram Schultz
- Department of Physiology, Development of Neuroscience, University of Cambridge, Cambridge CB3 0LT, UK
| | - Eyal Seidemann
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Yann-Suhan Senova
- Department of Neurosurgery, Assistance Publique-Hopitaux de Paris (APHP), U955 INSERM IMRB eq.15, University of Paris 12 UPEC, Faculté de Médecine, Créteil 94010, France
| | - Michael N Shadlen
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, The Kavli Institute for Brain Science & Howard Hughes Medical Institute, Columbia University, NY 10027, USA
| | - David L Sheinberg
- Department of Neuroscience and Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA
| | - Caitlin Siu
- Department of Ophthalmology, Moran Eye Institute, University of Utah, Salt Lake City, UT 84132, USA
| | - Yoland Smith
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Selina S Solomon
- Dominik P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Marc A Sommer
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - John L Spudich
- Department of Biochemistry and Molecular Biology, McGovern Medical School, The University of Texas-Houston, Houston, TX 77030, USA
| | - William R Stauffer
- Systems Neuroscience Institute, Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Masahiko Takada
- Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Shiming Tang
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Alexander Thiele
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE2 4HH, UK
| | - Stefan Treue
- German Primate Center - Leibniz Institute for Primate Research, 37077 Göttingen, Germany; Bernstein Center for Computational Neuroscience, Göttingen, Germany; Faculty for Biology and Psychology, University of Göttingen, Göttingen, Germany; Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Wim Vanduffel
- Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium; MGH Martinos Center, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02144, USA
| | - Rufin Vogels
- Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000 Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Matthew P Whitmire
- Departments of Psychology and Neuroscience, Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA
| | - Thomas Wichmann
- Yerkes National Primate Research Center, Morris K. Udall Center of Excellence for Parkinson's Disease, Department of Neurology, Emory University, GA 30329, USA
| | - Robert H Wurtz
- Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, MD 20982, USA
| | - Haoran Xu
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Azadeh Yazdan-Shahmorad
- Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; Departments of Bioengineering and Electrical and Computer Engineering, Washington National Primate Research Center, University of Washington, Seattle, WA 98105, USA
| | - Krishna V Shenoy
- Departments of Electrical Engineering, Bioengineering, and Neurobiology, Wu Tsai Neurosciences Institute and Bio-X Institute, Neuroscience Graduate Program, and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - James J DiCarlo
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Michael L Platt
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Marketing, Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
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12
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Chen Y, Ko H, Zemelman BV, Seidemann E, Nauhaus I. Uniform spatial pooling explains topographic organization and deviation from receptive-field scale invariance in primate V1. Nat Commun 2020; 11:6390. [PMID: 33319775 PMCID: PMC7738493 DOI: 10.1038/s41467-020-19954-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 11/06/2020] [Indexed: 11/29/2022] Open
Abstract
Receptive field (RF) size and preferred spatial frequency (SF) vary greatly across the primary visual cortex (V1), increasing in a scale invariant fashion with eccentricity. Recent studies reveal that preferred SF also forms a fine-scale periodic map. A fundamental open question is how local variability in preferred SF is tied to the overall spatial RF. Here, we use two-photon imaging to simultaneously measure maps of RF size, phase selectivity, SF bandwidth, and orientation bandwidth—all of which were found to be topographically organized and correlate with preferred SF. Each of these newly characterized inter-map relationships strongly deviate from scale invariance, yet reveal a common motif—they are all accounted for by a model with uniform spatial pooling from scale invariant inputs. Our results and model provide novel and quantitative understanding of the output from V1 to downstream circuits. Two-photon imaging in macaque V1 captured maps of tuning selectivity for four spatial parameters, all of which correlated with peak spatial frequency. These inter-map relationships reveal a common motif—they are described by uniform spatial pooling from a family of scale invariant Gabor receptive fields.
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Affiliation(s)
- Y Chen
- Department of Psychology, University of Texas, Austin, TX, USA.,Department of Neuroscience, University of Texas, Austin, TX, USA.,Center for Perceptual Systems, University of Texas, Austin, TX, USA
| | - H Ko
- Department of Psychology, University of Texas, Austin, TX, USA.,Center for Perceptual Systems, University of Texas, Austin, TX, USA
| | - B V Zemelman
- Department of Neuroscience, University of Texas, Austin, TX, USA.,Center for Learning and Memory, University of Texas, Austin, TX, USA
| | - E Seidemann
- Department of Psychology, University of Texas, Austin, TX, USA.,Department of Neuroscience, University of Texas, Austin, TX, USA.,Center for Perceptual Systems, University of Texas, Austin, TX, USA
| | - I Nauhaus
- Department of Psychology, University of Texas, Austin, TX, USA. .,Department of Neuroscience, University of Texas, Austin, TX, USA. .,Center for Perceptual Systems, University of Texas, Austin, TX, USA.
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13
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Li B, Routh BN, Johnston D, Seidemann E, Priebe NJ. Voltage-Gated Intrinsic Conductances Shape the Input-Output Relationship of Cortical Neurons in Behaving Primate V1. Neuron 2020; 107:185-196.e4. [PMID: 32348717 DOI: 10.1016/j.neuron.2020.04.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 01/02/2020] [Accepted: 03/31/2020] [Indexed: 12/01/2022]
Abstract
Neurons are input-output (I/O) devices-they receive synaptic inputs from other neurons, integrate those inputs with their intrinsic properties, and generate action potentials as outputs. To understand this fundamental process, we studied the interaction between synaptic inputs and intrinsic properties using whole-cell recordings from V1 neurons of awake, fixating macaque monkeys. Our measurements during spontaneous activity and visual stimulation reveal an intrinsic voltage-gated conductance that profoundly alters the integrative properties and visual responses of cortical neurons. This voltage-gated conductance increases neuronal gain and selectivity with subthreshold depolarization and linearizes the relationship between synaptic input and neural output. This intrinsic conductance is found in layer 2/3 V1 neurons of awake macaques, anesthetized mice, and acute brain slices. These results demonstrate that intrinsic conductances play an essential role in shaping the I/O relationship of cortical neurons and must be taken into account in future models of cortical computations.
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Affiliation(s)
- Baowang Li
- Center for Perceptual Systems, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA; Center for Learning and Memory, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA; Department of Psychology, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA
| | - Brandy N Routh
- Center for Learning and Memory, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA
| | - Daniel Johnston
- Center for Learning and Memory, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA
| | - Eyal Seidemann
- Center for Perceptual Systems, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA; Department of Psychology, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA.
| | - Nicholas J Priebe
- Center for Learning and Memory, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, 2415 Speedway, Austin, TX 78712, USA.
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14
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Chen SC, Benvenuti G, Whitmire MP, Chen Y, Seidemann E, Geisler WS. All-optical stimulation and imaging in macaque V1 reveals neural and behavioral masking effects of optogenetic stimulation in a threshold detection task. J Vis 2019. [DOI: 10.1167/19.10.144a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Spencer C Chen
- Center for Perceptual Systems, College of Liberal Arts, University of Texas at Austin
| | - Giacomo Benvenuti
- Center for Perceptual Systems, College of Liberal Arts, University of Texas at Austin
| | - Matthew P Whitmire
- Center for Perceptual Systems, College of Liberal Arts, University of Texas at Austin
| | - Yuzhi Chen
- Center for Perceptual Systems, College of Liberal Arts, University of Texas at Austin
| | - Eyal Seidemann
- Center for Perceptual Systems, College of Liberal Arts, University of Texas at Austin
| | - Wilson S Geisler
- Center for Perceptual Systems, College of Liberal Arts, University of Texas at Austin
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15
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Benvenuti G, Chen Y, Ramakrishnan C, Deisseroth K, Geisler WS, Seidemann E. Scale-Invariant Visual Capabilities Explained by Topographic Representations of Luminance and Texture in Primate V1. Neuron 2018; 100:1504-1512.e4. [PMID: 30392796 DOI: 10.1016/j.neuron.2018.10.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 09/02/2018] [Accepted: 10/09/2018] [Indexed: 11/28/2022]
Abstract
Humans have remarkable scale-invariant visual capabilities. For example, our orientation discrimination sensitivity is largely constant over more than two orders of magnitude of variations in stimulus spatial frequency (SF). Orientation-selective V1 neurons are likely to contribute to orientation discrimination. However, because at any V1 location neurons have a limited range of receptive field (RF) sizes, we predict that at low SFs V1 neurons will carry little orientation information. If this were the case, what could account for the high behavioral sensitivity at low SFs? Using optical imaging in behaving macaques, we show that, as predicted, V1 orientation-tuned responses drop rapidly with decreasing SF. However, we reveal a surprising coarse-scale signal that corresponds to the projection of the luminance layout of low-SF stimuli to V1's retinotopic map. This homeomorphic and distributed representation, which carries high-quality orientation information, is likely to contribute to our striking scale-invariant visual capabilities.
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Affiliation(s)
- Giacomo Benvenuti
- Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA; Department of Psychology, University of Texas, Austin, TX 78712, USA; Department of Neuroscience, University of Texas, Austin, TX 78712, USA
| | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA; Department of Psychology, University of Texas, Austin, TX 78712, USA; Department of Neuroscience, University of Texas, Austin, TX 78712, USA
| | - Charu Ramakrishnan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; CNC Program, Stanford University, Stanford, CA 94304, USA; Neurosciences Program, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Wilson S Geisler
- Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA; Department of Psychology, University of Texas, Austin, TX 78712, USA
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas, Austin, TX 78712, USA; Department of Psychology, University of Texas, Austin, TX 78712, USA; Department of Neuroscience, University of Texas, Austin, TX 78712, USA.
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Abstract
A long-term goal of visual neuroscience is to develop and test quantitative models that account for the moment-by-moment relationship between neural responses in early visual cortex and human performance in natural visual tasks. This review focuses on efforts to address this goal by measuring and perturbing the activity of primary visual cortex (V1) neurons while nonhuman primates perform demanding, well-controlled visual tasks. We start by describing a conceptual approach-the decoder linking model (DLM) framework-in which candidate decoding models take neural responses as input and generate predicted behavior as output. The ultimate goal in this framework is to find the actual decoder-the model that best predicts behavior from neural responses. We discuss key relevant properties of primate V1 and review current literature from the DLM perspective. We conclude by discussing major technological and theoretical advances that are likely to accelerate our understanding of the link between V1 activity and behavior.
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Affiliation(s)
- Eyal Seidemann
- Center for Perceptual Systems, University of Texas at Austin, Austin, Texas 78712, USA; ,
- Department of Psychology, University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, University of Texas at Austin, Austin, Texas 78712, USA
| | - Wilson S Geisler
- Center for Perceptual Systems, University of Texas at Austin, Austin, Texas 78712, USA; ,
- Department of Psychology, University of Texas at Austin, Austin, Texas 78712, USA
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17
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Seidemann E, Chen Y, Bai Y, Chen SC, Mehta P, Kajs BL, Geisler WS, Zemelman BV. Calcium imaging with genetically encoded indicators in behaving primates. eLife 2016; 5. [PMID: 27441501 PMCID: PMC4956408 DOI: 10.7554/elife.16178] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 06/16/2016] [Indexed: 11/30/2022] Open
Abstract
Understanding the neural basis of behaviour requires studying brain activity in behaving subjects using complementary techniques that measure neural responses at multiple spatial scales, and developing computational tools for understanding the mapping between these measurements. Here we report the first results of widefield imaging of genetically encoded calcium indicator (GCaMP6f) signals from V1 of behaving macaques. This technique provides a robust readout of visual population responses at the columnar scale over multiple mm2 and over several months. To determine the quantitative relation between the widefield GCaMP signals and the locally pooled spiking activity, we developed a computational model that sums the responses of V1 neurons characterized by prior single unit measurements. The measured tuning properties of the GCaMP signals to stimulus contrast, orientation and spatial position closely match the predictions of the model, suggesting that widefield GCaMP signals are linearly related to the summed local spiking activity. DOI:http://dx.doi.org/10.7554/eLife.16178.001 An important question in brain research is how neurons and the circuits they form process information to produce behavior. To understand what happens in a human brain, it is necessary to study a brain of similar complexity, such as that of a primate. Examining how the neurons in a brain region called the visual cortex process information about what we see is especially informative. This is because animals can be taught to perform different visual tasks, and because the visual cortex is relatively easy to access. In principle, therefore, it should be possible to use modern genetic and imaging techniques to study the primate visual system, but, until now, that has not been the case. Like much of the brain, the visual cortex consists of different classes of neurons that can excite, inhibit or modulate the activity of neighboring neurons. One way to study how these different classes of neurons interact with each other is to alter the animal’s DNA, such that only one cell type stands out during the experiment, allowing its role in the brain to be closely monitored. This technique has been used to study the interactions among neurons in the rodent brain, because rodent DNA is easy to alter. However, it is not easy to manipulate primate DNA. Seidemann et al. have, therefore, developed a new technique that can target a specific class of neurons, allowing the activity of just these cells to be distinguished from the rest. The method uses specially designed harmless viruses to produce foreign proteins in the excitatory neurons of the visual cortex in an adult macaque. The optical properties of the proteins change when the neuron they are in is active, allowing the activity of the excitatory neurons to be detected and tracked in awake animals while they perform a visual task. Previously, the activity of neurons in the primate visual cortex could only be measured using dyes that indiscriminately reported the activity of all the neurons present. Seidemann et al. found that, in addition to being more selective than the dye-based method, the new technique also more accurately depicted neuronal action potentials, which are the primary units of information in the brain. Seidemann et al. now plan to use a similar method to study the activity of the inhibitory neurons of the primate visual cortex. Further examination of both excitatory and inhibitory neurons at much higher magnification, using a different microscopy technique, will also reveal more subtle features of their responses during visual tasks. DOI:http://dx.doi.org/10.7554/eLife.16178.002
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Affiliation(s)
- Eyal Seidemann
- Center for Perceptual Systems, University of Texas, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States
| | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States
| | - Yoon Bai
- Center for Perceptual Systems, University of Texas, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States
| | - Spencer C Chen
- Center for Perceptual Systems, University of Texas, Austin, United States.,Department of Psychology, University of Texas, Austin, United States.,Department of Neuroscience, University of Texas, Austin, United States
| | - Preeti Mehta
- Department of Neuroscience, University of Texas, Austin, United States.,Center for Learning and Memory, University of Texas, Austin, United States
| | - Bridget L Kajs
- Department of Neuroscience, University of Texas, Austin, United States.,Center for Learning and Memory, University of Texas, Austin, United States
| | - Wilson S Geisler
- Center for Perceptual Systems, University of Texas, Austin, United States.,Department of Psychology, University of Texas, Austin, United States
| | - Boris V Zemelman
- Department of Neuroscience, University of Texas, Austin, United States.,Center for Learning and Memory, University of Texas, Austin, United States
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18
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Seidemann E. Attentional modulations of sub- and supra-threshold neural population responses in primate V1. J Vis 2015. [DOI: 10.1167/15.12.1416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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19
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Chen Y, Bai Y, Geisler W, Seidemann E. Inconsistencies between simultaneously measured neural and behavioral sensitivities in monkeys performing a fine orientation discrimination task. J Vis 2015. [DOI: 10.1167/15.12.1006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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20
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Bai Y, Chen Y, Geisler W, Seidemann E. Human and monkey detection performance in natural images compared with V1 population responses. J Vis 2015. [DOI: 10.1167/15.12.577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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21
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Seidemann E. Decision-related activity and top-down modulations at the level of neural populations in primate V1. J Vis 2014. [DOI: 10.1167/14.15.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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22
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Yang Z, Heeger DJ, Blake R, Seidemann E. Long-range traveling waves of activity triggered by local dichoptic stimulation in V1 of behaving monkeys. J Neurophysiol 2014; 113:277-94. [PMID: 25343785 DOI: 10.1152/jn.00610.2013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Traveling waves of cortical activity, in which local stimulation triggers lateral spread of activity to distal locations, have been hypothesized to play an important role in cortical function. However, there is conflicting physiological evidence for the existence of spreading traveling waves of neural activity triggered locally. Dichoptic stimulation, in which the two eyes view dissimilar monocular patterns, can lead to dynamic wave-like fluctuations in visual perception and therefore, provides a promising means for identifying and studying cortical traveling waves. Here, we used voltage-sensitive dye imaging to test for the existence of traveling waves of activity in the primary visual cortex of awake, fixating monkeys viewing dichoptic stimuli. We find clear traveling waves that are initiated by brief, localized contrast increments in one of the monocular patterns and then, propagate at speeds of ∼ 30 mm/s. These results demonstrate that under an appropriate visual context, circuitry in visual cortex in alert animals is capable of supporting long-range traveling waves triggered by local stimulation.
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Affiliation(s)
- Zhiyong Yang
- Brain and Behavior Discovery Institute, James and Jean Culver Vision Discovery Institute, and Department of Ophthalmology, Georgia Regents University, Augusta, Georgia
| | - David J Heeger
- Department of Psychology and Center for Neural Sciences, New York University, New York, New York
| | - Randolph Blake
- Vanderbilt Vision Research Center and Department of Psychology, Vanderbilt University, Nashville, Tennessee; Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea; and
| | - Eyal Seidemann
- Center for Perceptual Systems and Departments of Psychology and Neuroscience, University of Texas, Austin, Texas
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23
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Michel M, Chen Y, Geisler W, Seidemann E. A novel shape illusion predicted by the effect of local orientation on retinotopic-scale V1 population responses. J Vis 2012. [DOI: 10.1167/12.9.79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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24
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Chen Y, Seidemann E. Attentional modulations related to spatial gating but not to allocation of limited resources in primate V1. Neuron 2012; 74:557-66. [PMID: 22578506 DOI: 10.1016/j.neuron.2012.03.033] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2012] [Indexed: 11/29/2022]
Abstract
Attention can modulate neural responses in sensory cortical areas and improve behavioral performance in perceptual tasks. However, the nature and purpose of these modulations remain under debate. Here we used voltage-sensitive dye imaging (VSDI) to measure V1 population responses while monkeys performed a difficult detection task under focal or distributed attention. We found that V1 responses at attended locations are significantly elevated relative to actively ignored or irrelevant locations, consistent with the hypothesis that an important goal of attention in V1 is to highlight task-relevant information. Surprisingly, these modulations were indistinguishable under focal and distributed attention, suggesting a minor or no role for attention as a mechanism for allocating limited representational resources in V1. The response elevation at attended locations is additive, is widespread, and starts shortly before stimulus onset. This elevation could contribute to spatial gating by biasing competition in subsequent processing stages in favor of attended stimuli.
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Affiliation(s)
- Yuzhi Chen
- Center for Perceptual Systems, Department of Psychology and Section of Neurobiology, University of Texas at Austin, Austin, TX 78712, USA
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25
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Chen Y, Palmer CR, Seidemann E. The relationship between voltage-sensitive dye imaging signals and spiking activity of neural populations in primate V1. J Neurophysiol 2012; 107:3281-95. [PMID: 22422999 DOI: 10.1152/jn.00977.2011] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Voltage-sensitive dye imaging (VSDI) is a powerful technique for measuring neural population responses from a large cortical region simultaneously with millisecond temporal resolution and columnar spatial resolution. However, the relationship between the average VSDI signal and the average spiking activity of neural populations is largely unknown. To better understand this relationship, we compared visual responses measured from V1 of behaving monkeys using VSDI and single-unit electrophysiology. We found large and systematic differences between position and orientation tuning properties obtained with these two techniques. We then determined that a simple computational model could explain these tuning differences. This model, together with our experimental results, allowed us to estimate the quantitative relationship between the average VSDI signal and local spiking activity. We found that this relationship is similar to the previously reported nonlinear relationship between average membrane potential and spike rate in single V1 neurons, suggesting that VSDI signals are dominated by subthreshold synaptic activity. This model, together with the VSDI measured maps for spatial position (retinotopy) and orientation, also allowed us to estimate the spatial integration area over which neural responses contribute to the VSDI signal at a given location. We found that the VSDI-integration area is consistent with a Gaussian envelope with a space constant of ∼230 μm. Finally, we show how this model and estimated parameters can be used to predict the pattern of population responses at the level of spiking activity from VSDI responses.
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Affiliation(s)
- Yuzhi Chen
- Department of Psychology and Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA
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26
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Abstract
What are the shape and size of the region in primate V1 that processes information from a single point in visual space? This region, a fundamental property termed cortical point image (CPI) (McIlwain 1986), represents the minimal population of V1 neurons that can be activated by a visual stimulus and therefore has important implications for population coding in the cortex. Previous indirect attempts to measure the CPI in macaque V1 using sparse microelectrode recordings resulted in conflicting findings. Whereas some early studies suggested that CPI size is constant throughout V1 (e.g., Hubel and Wiesel 1974), others have reported large changes in CPI size in parafoveal V1 (e.g., Van Essen et al. 1984). To resolve this controversy, we used voltage-sensitive dye imaging in V1 of fixating monkeys to directly measure the subthreshold CPI and several related properties across a range of parafoveal eccentricities. We found that despite large changes in other properties of the retinotopic map, the subthreshold CPI is approximately constant and extends over ∼6 × 8 mm(2). This large and invariant CPI ensures a uniform representation of each point in visual space, with a complete representation of all visual features in V1, as originally proposed by Hubel and Wiesel (1974). In addition, we found several novel and unexpected asymmetries and anisotropies in the shapes of the CPI and the population receptive field. These results expand our understanding of the representation of visual space in V1 and are likely to be relevant for the representations of stimuli in other sensory cortical areas.
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Affiliation(s)
- Chris R Palmer
- Dept. of Psychology and Center for Perceptual Systems, The Univ. of Texas at Austin, 1 Univ. Station A8000, Austin, TX 78712-0187, USA
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27
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Seidemann E. Complex spatiotemporal dynamics of V1 population responses explained by a simple gain-control model. J Vis 2010. [DOI: 10.1167/10.15.32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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29
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Seidemann E, Geisler W, Chen Y. Optimal decoding of neural population responses in the primate visual cortex. J Vis 2010. [DOI: 10.1167/7.15.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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30
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Sit YF, Chen Y, Geisler WS, Miikkulainen R, Seidemann E. Complex dynamics of V1 population responses explained by a simple gain-control model. Neuron 2010; 64:943-56. [PMID: 20064399 DOI: 10.1016/j.neuron.2009.08.041] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2009] [Indexed: 10/20/2022]
Abstract
To understand sensory encoding and decoding, it is essential to characterize the dynamics of population responses in sensory cortical areas. Using voltage-sensitive dye imaging in awake, fixating monkeys, we obtained complete quantitative measurements of the spatiotemporal dynamics of V1 responses over the entire region activated by small, briefly presented stimuli. The responses exhibit several complex properties: they begin to rise approximately simultaneously over the entire active region, but reach their peak more rapidly at the center. However, at stimulus offset the responses fall simultaneously and at the same rate at all locations. Although response onset depends on stimulus contrast, both the peak spatial profile and the offset dynamics are independent of contrast. We show that these results are consistent with a simple population gain-control model that generalizes earlier single-neuron contrast gain-control models. This model provides valuable insight and is likely to be applicable to other brain areas.
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Affiliation(s)
- Yiu Fai Sit
- Department of Computer Sciences, The University of Texas at Austin, 1 University Station, A8000, Austin, TX 78712, USA
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31
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Chen Y, Geisler WS, Seidemann E. Optimal temporal decoding of neural population responses in a reaction-time visual detection task. J Neurophysiol 2008; 99:1366-79. [PMID: 18199810 DOI: 10.1152/jn.00698.2007] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Behavioral performance in detection and discrimination tasks is likely to be limited by the quality and nature of the signals carried by populations of neurons in early sensory cortical areas. Here we used voltage-sensitive dye imaging (VSDI) to directly measure neural population responses in the primary visual cortex (V1) of monkeys performing a reaction-time detection task. Focusing on the temporal properties of the population responses, we found that V1 responses are consistent with a stimulus-evoked response with amplitude and latency that depend on target contrast and a stimulus-independent additive noise with long-lasting temporal correlations. The noise had much lower amplitude than the ongoing activity reported previously in anesthetized animals. To understand the implications of these properties for subsequent processing stages that mediate behavior, we derived the Bayesian ideal observer that specifies how to optimally use neural responses in reaction time tasks. Using the ideal observer analysis, we show that 1) the observed temporal correlations limit the performance benefit that can be attained by accumulating V1 responses over time, 2) a simple temporal decorrelation operation with time-lagged excitation and inhibition minimizes the detrimental effect of these correlations, 3) the neural information relevant for target detection is concentrated in the initial response following stimulus onset, and 4) a decoder that optimally uses V1 responses far outperforms the monkey in both speed and accuracy. Finally, we demonstrate that for our particular detection task, temporal decorrelation followed by an appropriate running integrator can approach the speed and accuracy of the optimal decoder.
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Affiliation(s)
- Yuzhi Chen
- Department of Psychology and Center for Perceptual Systems, The University of Texas at Austin, 108 E. Dean Keeton, 1 University Station A8000, Austin, TX 78712-0187, USA
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32
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Abstract
Perceptual decisions are likely to be based on signals that are provided by populations of neurons in early sensory cortical areas. How these neural responses are combined across neurons and over time to mediate behavior is unknown. To study the link between neural responses and perceptual decisions, we recorded the activity of single units (SU) and multiple units (MU) in the primary visual cortex (V1) of monkeys while they performed a reaction-time visual detection task. We then determined how well the target could be detected from these neural signals. We found that, on average, the detection sensitivities supported by SU and MU in V1 are comparable with the detection sensitivity of the monkey even when considering neural responses during brief temporal intervals (median duration, 137 ms) that ended shortly before the monkey's reaction time. However, we observed systematic differences between the overall shape of the neurometric functions and the monkey's psychometric functions. We also examined the quantitative relationship between SU and MU activity and found that MU responses are consistent with the sum of the responses of multiple SU, most of which have low stimulus selectivity. Finally, we found weak but significant trial-to-trial covariations between V1 activity and behavioral choices, demonstrating for the first time that choice probability can be observed at the earliest stages of cortical sensory processing. Together, these results suggest that the activity of a large population of V1 neurons is combined suboptimally by subsequent processing stages to mediate behavioral performance in visual detection tasks.
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Affiliation(s)
- Chris Palmer
- Department of Psychology and Center for Perceptual Systems, University of Texas at Austin, Austin, Texas 78712
| | - Shao-Ying Cheng
- Department of Psychology and Center for Perceptual Systems, University of Texas at Austin, Austin, Texas 78712
| | - Eyal Seidemann
- Department of Psychology and Center for Perceptual Systems, University of Texas at Austin, Austin, Texas 78712
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33
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Yang Z, Heeger DJ, Seidemann E. Rapid and precise retinotopic mapping of the visual cortex obtained by voltage-sensitive dye imaging in the behaving monkey. J Neurophysiol 2007; 98:1002-1014. [PMID: 17522170 DOI: 10.1109/tmi.2012.2196707.separate] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2023] Open
Abstract
Retinotopy is a fundamental organizing principle of the visual cortex. Over the years, a variety of techniques have been used to examine it. None of these techniques, however, provides a way to rapidly characterize retinotopy, at the submillimeter range, in alert, behaving subjects. Voltage-sensitive dye imaging (VSDI) can be used to monitor neuronal population activity at high spatial and temporal resolutions. Here we present a VSDI protocol for rapid and precise retinotopic mapping in the behaving monkey. Two monkeys performed a fixation task while thin visual stimuli swept periodically at a high speed in one of two possible directions through a small region of visual space. Because visual space is represented systematically across the cortical surface, each moving stimulus produced a traveling wave of activity in the cortex that could be precisely measured with VSDI. The time at which the peak of the traveling wave reached each location in the cortex linked this location with its retinotopic representation. We obtained detailed retinotopic maps from a region of about 1 cm(2) over the dorsal portion of areas V1 and V2. Retinotopy obtained during <4 min of imaging had a spatial precision of 0.11-0.19 mm, was consistent across experiments, and reliably predicted the locations of the response to small localized stimuli. The ability to rapidly obtain precise retinotopic maps in behaving monkeys opens the door for detailed analysis of the relationship between spatiotemporal dynamics of population responses in the visual cortex and perceptually guided behavior.
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Affiliation(s)
- Zhiyong Yang
- Department of Psychology and Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712-0187, USA
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34
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Yang Z, Heeger DJ, Seidemann E. Rapid and precise retinotopic mapping of the visual cortex obtained by voltage-sensitive dye imaging in the behaving monkey. J Neurophysiol 2007; 98:1002-14. [PMID: 17522170 PMCID: PMC2214852 DOI: 10.1152/jn.00417.2007] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Retinotopy is a fundamental organizing principle of the visual cortex. Over the years, a variety of techniques have been used to examine it. None of these techniques, however, provides a way to rapidly characterize retinotopy, at the submillimeter range, in alert, behaving subjects. Voltage-sensitive dye imaging (VSDI) can be used to monitor neuronal population activity at high spatial and temporal resolutions. Here we present a VSDI protocol for rapid and precise retinotopic mapping in the behaving monkey. Two monkeys performed a fixation task while thin visual stimuli swept periodically at a high speed in one of two possible directions through a small region of visual space. Because visual space is represented systematically across the cortical surface, each moving stimulus produced a traveling wave of activity in the cortex that could be precisely measured with VSDI. The time at which the peak of the traveling wave reached each location in the cortex linked this location with its retinotopic representation. We obtained detailed retinotopic maps from a region of about 1 cm(2) over the dorsal portion of areas V1 and V2. Retinotopy obtained during <4 min of imaging had a spatial precision of 0.11-0.19 mm, was consistent across experiments, and reliably predicted the locations of the response to small localized stimuli. The ability to rapidly obtain precise retinotopic maps in behaving monkeys opens the door for detailed analysis of the relationship between spatiotemporal dynamics of population responses in the visual cortex and perceptually guided behavior.
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Affiliation(s)
- Zhiyong Yang
- Department of Psychology and Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712-0187, USA
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35
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Chen Y, Geisler WS, Seidemann E. Optimal decoding of correlated neural population responses in the primate visual cortex. Nat Neurosci 2006; 9:1412-20. [PMID: 17057706 PMCID: PMC1851689 DOI: 10.1038/nn1792] [Citation(s) in RCA: 145] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2006] [Accepted: 09/26/2006] [Indexed: 11/09/2022]
Abstract
Even the simplest environmental stimuli elicit responses in large populations of neurons in early sensory cortical areas. How these distributed responses are read out by subsequent processing stages to mediate behavior remains unknown. Here we used voltage-sensitive dye imaging to measure directly population responses in the primary visual cortex (V1) of monkeys performing a demanding visual detection task. We then evaluated the ability of different decoding rules to detect the target from the measured neural responses. We found that small visual targets elicit widespread responses in V1, and that response variability at distant sites is highly correlated. These correlations render most previously proposed decoding rules inefficient relative to one that uses spatially antagonistic center-surround summation. This optimal decoder consistently outperformed the monkey in the detection task, demonstrating the sensitivity of our techniques. Overall, our results suggest an unexpected role for inhibitory mechanisms in efficient decoding of neural population responses.
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Affiliation(s)
- Yuzhi Chen
- Department of Psychology and Center for Perceptual Systems, 1 University Station, A8000, University of Texas at Austin, Austin, Texas 78712, USA
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36
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Abstract
The frontal eye field and neighboring area 8Ar of the primate cortex are involved in programming and execution of saccades. Electrical microstimulation in these regions elicits short-latency contralateral saccades. To determine how spatiotemporal dynamics of microstimulation-evoked activity are converted into saccade plans, we used a combination of real-time optical imaging and microstimulation in behaving monkeys. Short stimulation trains evoked a rapid and widespread wave of depolarization followed by unexpected large and prolonged hyperpolarization. During this hyperpolarization saccades are almost exclusively ipsilateral, suggesting an important role for hyperpolarization in determining saccade goal.
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Affiliation(s)
- Eyal Seidemann
- Department of Neurobiology and the Grodetsky Center for Studies of Higher Brain Function, Weizmann Institute of Science, Rehovot 76100, Israel.
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37
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Abstract
The relationship between the neural processing of color and motion information has been a contentious issue in visual neuroscience. We examined this relationship directly by measuring neural responses to isoluminant S cone signals in extrastriate area MT of the macaque monkey. S cone stimuli produced robust, direction-selective responses at most recording sites, indicating that color signals are present in MT. While these responses were unequivocal, S cone contrast sensitivity was, on average, 1.0-1.3 log units lower than luminance contrast sensitivity. The presence of S cone responses and the relative sensitivity of MT neurons to S cone and luminance signals agree with functional magnetic resonance imaging (fMRI) measurements in human MT+. The results are consistent with the hypothesis that color signals in MT influence behavior in speed judgment tasks.
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Affiliation(s)
- E Seidemann
- Howard Hughes Medical Institute, Department of Neurobiology, Stanford University, California 94305, USA
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38
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Heeger DJ, Boynton GM, Demb JB, Seidemann E, Newsome WT. Motion opponency in visual cortex. J Neurosci 1999; 19:7162-74. [PMID: 10436069 PMCID: PMC6782843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/1998] [Revised: 06/01/1999] [Accepted: 06/03/1999] [Indexed: 02/13/2023] Open
Abstract
Perceptual studies suggest that visual motion perception is mediated by opponent mechanisms that correspond to mutually suppressive populations of neurons sensitive to motions in opposite directions. We tested for a neuronal correlate of motion opponency using functional magnetic resonance imaging (fMRI) to measure brain activity in human visual cortex. There was strong motion opponency in a secondary visual cortical area known as the human MT complex (MT+), but there was little evidence of motion opponency in primary visual cortex. To determine whether the level of opponency in human and monkey are comparable, a variant of these experiments was performed using multiunit electrophysiological recording in areas MT and MST of the macaque monkey brain. Although there was substantial variability in the degree of opponency between recording sites, the monkey and human data were qualitatively similar on average. These results provide further evidence that: (1) direction-selective signals underly human MT+ responses, (2) neuronal signals in human MT+ support visual motion perception, (3) human MT+ is homologous to macaque monkey MT and adjacent motion sensitive brain areas, and (4) that fMRI measurements are correlated with average spiking activity.
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Affiliation(s)
- D J Heeger
- Department of Psychology, Stanford University, Stanford, California 94305-2130, USA
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39
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Abstract
This study examines the influence of spatial attention on the responses of neurons in the middle temporal visual area (MT or V5) of extrastriate cortex. Two monkeys were trained to perform a direction-discrimination task. On each trial, two apertures of random-dot stimuli appeared simultaneously at two spatially separated locations; the monkeys were required to discriminate the direction of stimulus motion at one location while ignoring the stimulus at the other location. After extensive training, we recorded the responses of MT neurons in two configurations: 1) Both apertures placed "within" the neuron's receptive field (RF) and 2) one aperture covering the RF while the other was presented at a "remote" location. For each unit we compared the responses to identical stimulus displays when the monkey was instructed to attend to one or the other aperture. The responses of MT neurons were 8.7% stronger, on average, when the monkey attended to the spatial location that contained motion in the "preferred" direction. Attentional effects were equal, on average, in the within RF and remote configurations. The attentional modulations began approximately 300 ms after stimulus onset, gradually increased throughout the trial, and peaked near stimulus offset. An analysis of the neuronal responses on error trials suggests that the monkeys failed to attend to the appropriate spatial location on these trials. The relatively weak attentional effects that we observed contrast strikingly with recent results of Treue and Maunsell, who demonstrated very strong attentional modulations (median effect >80%) in MT in a task that shares many features with ours. Our results suggest that spatial attention alone is not sufficient to induce strong attentional effects in MT even when two competing motion stimuli appear within the RF of the recorded neuron. The difference between our results and those of Treue and Maunsell suggests that the magnitude of the attentional effects in MT may depend critically on how attention is directed to a particular stimulus and on the precise demands of the task.
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Affiliation(s)
- E Seidemann
- Howard Hughes Medical Institute and Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305, USA
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40
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Abstract
The flow of neural signals within the cerebral cortex must be subject to multiple controls as behaviour unfolds in time. In a visual discrimination task that includes a delay period, the transmission of sensory signals to circuitry that mediates memory, decision-making and motor-planning must be governed closely by 'filtering' or 'gating' mechanisms so that extraneous events occurring before, during or after presentation of the critical visual stimulus have little or no effect on the subject's behavioural responses. Here we study one such mechanism physiologically by applying electrical microstimulation to columns of directionally selective neurons in the middle temporal visual area at varying times during single trials of a direction-discrimination task. The behavioural effects of microstimulation varied strikingly according to the timing of delivery within the trial, indicating that signals produced by microstimulation may be subject to active 'gating'. Our results show several important features of this gating process: first, signal flow is modulated upwards on onset of the visual stimulus and downwards, typically with a slower time course, after stimulus offset; second, gating efficacy can be modified by behavioural training; and third, gating is implemented primarily downstream of the middle temporal visual area.
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Affiliation(s)
- E Seidemann
- Howard Hughes Medical Institute, Department of Neurobiology, Stanford University School of Medicine, California 94305, USA.
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41
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Abstract
Pronounced effects of attention have been demonstrated in a region of visual cortex previously thought to be devoid of such influences; identifying the features critical for eliciting these effects should teach us a great deal about the neural underpinnings of visual attention.
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Affiliation(s)
- J M Groh
- Department of Neurobiology, Stanford University School of Medicine, California 94305, USA
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42
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Seidemann E, Meilijson I, Abeles M, Bergman H, Vaadia E. Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task. J Neurosci 1996; 16:752-68. [PMID: 8551358 PMCID: PMC6578656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
To test whether spiking activity of six to eight simultaneously recorded neurons in the frontal cortex of a monkey can be characterized by a sequence of discrete and stable states, neuronal activity is analyzed by a hidden Markov model (HMM). Using the HMM method, we are able to detect distinct states of neuronal activity within which firing rates are approximately stationary. Transitions between states, as expressed by concomitant changes in the firing rates of several units, occur quite abruptly. The significance and consistency of the states are confirmed by comparison with simulated data. The detected states are specific to a monkey's response in a delayed localization task, allowing correct prediction of the response in approximately 90% of the trials. Similar predictive power is achieved by a model based simply on the response histograms (PSTH) of the units. The two models reach this predictive ability with different time courses: the PSTH model gains predictive power with a higher rate in the first second of the delay, and the HMM gains predictive power with higher rate in the next 3 sec. In this later period, conventional methods such as the PSTH cannot detect any firing rate modulations, but the HMM successfully captures transitions between distinct states that are specific to the monkey's behavioral response and occur at highly variable times from trial to trial. Our results suggest that neuronal activity in this later period is described best as transitions among distinct states that may reflect discrete steps in the monkey's mental processes.
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Affiliation(s)
- E Seidemann
- School of Mathematical Sciences, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Ramat Aviv, Israel
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43
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Abstract
Parallel recordings of spike trains of several single cortical neurons in behaving monkeys were analyzed as a hidden Markov process. The parallel spike trains were considered as a multivariate Poisson process whose vector firing rates change with time. As a consequence of this approach, the complete recording can be segmented into a sequence of a few statistically discriminated hidden states, whose dynamics are modeled as a first-order Markov chain. The biological validity and benefits of this approach were examined in several independent ways: (i) the statistical consistency of the segmentation and its correspondence to the behavior of the animals; (ii) direct measurement of the collective flips of activity, obtained by the model; and (iii) the relation between the segmentation and the pair-wise short-term cross-correlations between the recorded spike trains. Comparison with surrogate data was also carried out for each of the above examinations to assure their significance. Our results indicated the existence of well-separated states of activity, within which the firing rates were approximately stationary. With our present data we could reliably discriminate six to eight such states. The transitions between states were fast and were associated with concomitant changes of firing rates of several neurons. Different behavioral modes and stimuli were consistently reflected by different states of neural activity. Moreover, the pair-wise correlations between neurons varied considerably between the different states, supporting the hypothesis that these distinct states were brought about by the cooperative action of many neurons.
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Affiliation(s)
- M Abeles
- School of Medicine, Hebrew University, Jerusalem, Israel
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44
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Lancet D, Sadovsky E, Seidemann E. Probability model for molecular recognition in biological receptor repertoires: significance to the olfactory system. Proc Natl Acad Sci U S A 1993; 90:3715-9. [PMID: 8475121 PMCID: PMC46372 DOI: 10.1073/pnas.90.8.3715] [Citation(s) in RCA: 117] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
A generalized phenomenological model is presented for stereospecific recognition between biological receptors and their ligands. We ask what is the distribution of binding constants psi(K) between an arbitrary ligand and members of a large receptor repertoire, such as immunoglobulins or olfactory receptors. For binding surfaces with B potential subsite and S different types of subsite configurations, the number of successful elementary interactions obeys a binomial distribution. The discrete probability function psi(K) is then derived with assumptions on alpha, the free energy contribution per elementary interaction. The functional form of psi(K) may be universal, although the parameter values could vary for different ligand types. An estimate of the parameter values of psi(K) for iodovanillin, an analog of odorants and immunological haptens, is obtained by equilibrium dialysis experiments with nonimmune antibodies. Based on a simple relationship, predicted by the model, between the size of a receptor repertoire and its average maximal affinity toward an arbitrary ligand, the size of the olfactory receptor repertoire (Nolf) is calculated as 300-1000, in very good agreement with recent molecular biological studies. A very similar estimate, Nolf = 500, is independently derived by relating a theoretical distribution of maxima for psi(K) with published human olfactory threshold variations. The present model also has implications to the question of olfactory coding and to the analysis of specific anosmias, genetic deficits in perceiving particular odorants. More generally, the proposed model provides a better understanding of ligand specificity in biological receptors and could help in understanding their evolution.
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Affiliation(s)
- D Lancet
- Department of Membrane Research and Biophysics, Weizmann Institute of Science, Rehovot, Israel
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Lancet D, Gross-Isseroff R, Margalit T, Seidemann E, Ben-Arie N. Olfaction: from signal transduction and termination to human genome mapping. Chem Senses 1993. [DOI: 10.1093/chemse/18.2.217] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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